<!-- This comment will put IE 6, 7 and 8 in quirks mode -->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<title>include/shark/Models/ConvolutionalModel.h Source File</title>
<script type="text/javaScript" src="search/search.js"></script>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3.0.1/es5/tex-mml-chtml.js"></script>
<script src="../../mlstyle.js"></script>
<link href="../css/besser.css" rel="stylesheet" type="text/css"/>
</head>
<!-- pretty cool: each body gets an id tag which is the basename of the web page  -->
<!--              and allows for page-specific CSS. this is client-side scripted, -->
<!--              so the id will not yet show up in the served source code -->
<script type="text/javascript">
    jQuery(document).ready(function () {
        var url = jQuery(location).attr('href');
        var pname = url.substr(url.lastIndexOf("/")+1, url.lastIndexOf(".")-url.lastIndexOf("/")-1);
        jQuery('#this_url').html('<strong>' + pname + '</strong>');
        jQuery('body').attr('id', pname);
    });
</script>
<body>
    <div id="shark_old">
        <div id="wrap">
            <div id="header">
                <div id="site-name"><a href="../../sphinx_pages/build/html/index.html">Shark machine learning library</a></div>
                <ul id="nav">
                    <li >
                        <a href="../../sphinx_pages/build/html/rest_sources/installation.html">Installation</a>
                    </li>
		    <li >
                        <a href="../../sphinx_pages/build/html/rest_sources/tutorials/tutorials.html">Tutorials</a>
                    </li>
		    <li >
                        <a href="../../sphinx_pages/build/html/rest_sources/benchmark.html">Benchmarks</a>
                    </li>
                    <li class="active">
                        <a href="classes.html">Documentation</a>
                        <ul>
                            <li class="first"></li>
                            <li><a href="../../sphinx_pages/build/html/rest_sources/quickref/quickref.html">Quick references</a></li>
                            <li><a href="classes.html">Class list</a></li>
                            <li class="last"><a href="group__shark__globals.html">Global functions</a></li>
                        </ul>
                    </li>
                </ul>
            </div>
        </div>
    </div>
<div id="doxywrapper">
<!--
    <div id="global_doxytitle">Doxygen<br>Documentation:</div>
-->
    <div id="navrow_wrapper">
<!-- Generated by Doxygen 1.9.8 -->
<div id="nav-path" class="navpath">
  <ul>
<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_9d0c4981f10d03078bcfd5c74fe41ce8.html">shark</a></li><li class="navelem"><a class="el" href="dir_d88670438e31f34fbe7ccda1c525ad9e.html">Models</a></li>  </ul>
</div>
</div><!-- top -->
<div class="header">
  <div class="headertitle"><div class="title">ConvolutionalModel.h</div></div>
</div><!--header-->
<div class="contents">
<a href="_convolutional_model_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> *</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       Implements a model applying a convolution to an image</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> *</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * \author    </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \date        2017</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> *</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> *</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> *</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> *</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> *</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> */</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#ifndef SHARK_MODELS_CONV2DModel_H</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#define SHARK_MODELS_CONV2DModel_H</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_model_8h.html">shark/Models/AbstractModel.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_neuron_layers_8h.html">shark/Models/NeuronLayers.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#include &lt;<a class="code" href="conv2d_8hpp.html">shark/LinAlg/BLAS/kernels/conv2d.hpp</a>&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#include &lt;<a class="code" href="_padding_8h.html">shark/Core/Images/Padding.h</a>&gt;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_reorder_8h.html">shark/Core/Images/Reorder.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment"></span> </div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">///</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">/// \brief Convolutional Model for 2D image data.</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">///</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// This model computes the result of</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// \f$ y = f(x) = g(\text{convolution}(w, x) + b) \f$, where g is an arbitrary activation function  \ref activations and</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// convolution is the convolution of the input image x with the filters w.  b is a vector with one entry for each filter which is then applied to each response above</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// The image is allowed to have several channels and are linearized to a single vector of size width * height * numChannels.</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// This is done by itnerleaving channels, i.e. for a pixel all channels are stored contiguously. Then the pixels are stored in</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// a row-major scheme.</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">///</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// For handling edge condition, the Conv2D model handles two different convolution modes:</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">///</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// Padding::Valid:</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// The output is only computed on patches which are fully inside the unpadded image as a linearized vector in the same format</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// of size (width - filter_width+1) * (height - filter_height+1) * numFilters.</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">///</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// Padding::ZeroPad</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">/// The output input is padded with zeros and the output has the same size as the input</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// of size width * height * numFilters.</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">///</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// \ingroup models</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> VectorType = RealVector, <span class="keyword">class</span> ActivationFunction = LinearNeuron&gt;</div>
<div class="foldopen" id="foldopen00066" data-start="{" data-end="};">
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html">   66</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_conv2_d_model.html" title="Convolutional Model for 2D image data.">Conv2DModel</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_model.html" title="Base class for all Models.">AbstractModel</a>&lt;VectorType,VectorType,VectorType&gt;{</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_model.html" title="Base class for all Models.">AbstractModel&lt;VectorType,VectorType, VectorType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_model.html">base_type</a>;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_conv2_d_model.html" title="Convolutional Model for 2D image data.">Conv2DModel&lt;VectorType, ActivationFunction&gt;</a> <a class="code hl_class" href="classshark_1_1_conv2_d_model.html" title="Convolutional Model for 2D image data.">self_type</a>;</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> VectorType::value_type value_type;</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> VectorType::device_type device_type;</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span> </div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    <span class="keyword">static_assert</span>(!std::is_same&lt;typename VectorType::storage_type::storage_tag, blas::dense_tag&gt;::value, <span class="stringliteral">&quot;Conv2D not implemented for sparse inputs&quot;</span>);</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">   75</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#aa0c72e230b9a1324c95ba8ac0b07ba13" title="defines the batch type of the output type">base_type::BatchOutputType</a> <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a>;</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#ae73b28ed0cf6fa128617f5ec57890362">   76</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#a518304e95092673b7b6438cace052ef6" title="defines the batch type of the input type.">base_type::BatchInputType</a> <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#ae73b28ed0cf6fa128617f5ec57890362">BatchInputType</a>;</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a1a6bf16a226df283254eed73c480b280">   77</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_i_parameterizable.html#a2ad5e2e60b2b352988b41f46024d790b">base_type::ParameterVectorType</a> <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a1a6bf16a226df283254eed73c480b280">ParameterVectorType</a>;</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment"></span> </div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">    /// Default Constructor; use setStructure later.</span></div>
<div class="foldopen" id="foldopen00080" data-start="{" data-end="}">
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a6ca37037a3ff8605a970d45ecad0090b">   80</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a6ca37037a3ff8605a970d45ecad0090b" title="Default Constructor; use setStructure later.">Conv2DModel</a>(){</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <a class="code hl_variable" href="classshark_1_1_abstract_model.html#a4c5a689901083e50007f53de72f694fc">base_type::m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_model.html#a76a2d024b6013037b072596fe4f9f829a89a819e2614f818baa23c5c8fdd4393d">base_type::HAS_FIRST_PARAMETER_DERIVATIVE</a>;</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <a class="code hl_variable" href="classshark_1_1_abstract_model.html#a4c5a689901083e50007f53de72f694fc">base_type::m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_model.html#a76a2d024b6013037b072596fe4f9f829a82b5c22e1b95f20aff01b4b39e86c607">base_type::HAS_FIRST_INPUT_DERIVATIVE</a>;</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    }</div>
</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    <span class="comment"></span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">    ///\brief Sets the structure by setting the dimensionalities of image and filters.</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">    /// \arg imageShape Shape of the image imHeight x imWidth x channel</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">    /// \arg filterShape Shape of the filter matrix numFilters x fiHeight x fiWidth x channel</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">    /// \arg type Type of convolution padding to perform</span></div>
<div class="foldopen" id="foldopen00090" data-start="{" data-end="}">
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#adbedf083eca0fe5c44d1a2e6ee09cef2">   90</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#adbedf083eca0fe5c44d1a2e6ee09cef2" title="Sets the structure by setting the dimensionalities of image and filters.">Conv2DModel</a>(</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <span class="keyword">const</span>&amp; imageShape, <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <span class="keyword">const</span>&amp; filterShape, <a class="code hl_enumeration" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035">Padding</a> type = <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a5281170e19a4d157693a263c68546214">Padding::ZeroPad</a></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    ){</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <a class="code hl_variable" href="classshark_1_1_abstract_model.html#a4c5a689901083e50007f53de72f694fc">base_type::m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_model.html#a76a2d024b6013037b072596fe4f9f829a89a819e2614f818baa23c5c8fdd4393d">base_type::HAS_FIRST_PARAMETER_DERIVATIVE</a>;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        <a class="code hl_variable" href="classshark_1_1_abstract_model.html#a4c5a689901083e50007f53de72f694fc">base_type::m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_model.html#a76a2d024b6013037b072596fe4f9f829a82b5c22e1b95f20aff01b4b39e86c607">base_type::HAS_FIRST_INPUT_DERIVATIVE</a>;</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a7e816b41a71a79b5f9da6bf304837b21" title="Sets the structure by setting the shape of image and filters.">setStructure</a>(imageShape, filterShape, type);</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    }</div>
</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span> </div>
<div class="foldopen" id="foldopen00098" data-start="{" data-end="}">
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a918f1703be09907f032fad65acabacba">   98</a></span>    std::string <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a918f1703be09907f032fad65acabacba" title="returns the name of the object">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;Conv2DModel&quot;</span>; }</div>
</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    <span class="comment"></span></div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment">    ///\brief Returns the expected shape of the input</span></div>
<div class="foldopen" id="foldopen00102" data-start="{" data-end="}">
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a9cfa029e47ed8ede811b331830a45061">  102</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a9cfa029e47ed8ede811b331830a45061" title="Returns the expected shape of the input.">inputShape</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <span class="keywordflow">return</span> {m_imageHeight, m_imageWidth, m_numChannels};</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    ///\brief Returns the shape of the output</span></div>
<div class="foldopen" id="foldopen00106" data-start="{" data-end="}">
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f">  106</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <span class="keywordflow">if</span>(m_type != <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a3ac705f2acd51a4613f9188c05c91d0d">Padding::Valid</a>){</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>            <span class="keywordflow">return</span> {m_imageHeight, m_imageWidth, m_numFilters};</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            <span class="keywordflow">return</span> {m_imageHeight - m_filterHeight + 1, m_imageWidth - m_filterWidth + 1, m_numFilters};</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        }</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>    }</div>
</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>    <span class="comment"></span></div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">    /// \brief Returns the activation function.</span></div>
<div class="foldopen" id="foldopen00115" data-start="{" data-end="}">
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#af5b1b148205a549e9fd13fba87d5ad62">  115</a></span><span class="comment"></span>    ActivationFunction <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#af5b1b148205a549e9fd13fba87d5ad62" title="Returns the activation function.">activationFunction</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        <span class="keywordflow">return</span> m_activation;</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    }</div>
</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    <span class="comment"></span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">    /// \brief Returns the activation function.</span></div>
<div class="foldopen" id="foldopen00120" data-start="{" data-end="}">
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a19617ce611c2b116d5402b33bd64ec59">  120</a></span><span class="comment"></span>    ActivationFunction&amp; <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a19617ce611c2b116d5402b33bd64ec59" title="Returns the activation function.">activationFunction</a>(){</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="keywordflow">return</span> m_activation;</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    }</div>
</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment"></span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// \brief Obtain the parameter vector.</span></div>
<div class="foldopen" id="foldopen00125" data-start="{" data-end="}">
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a65a69c4c0b9fe12db3950e04d625be7e">  125</a></span><span class="comment"></span>    <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a1a6bf16a226df283254eed73c480b280">ParameterVectorType</a> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a65a69c4c0b9fe12db3950e04d625be7e" title="Obtain the parameter vector.">parameterVector</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <span class="keywordflow">return</span> m_filters | m_offset;</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    }</div>
</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment"></span> </div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    /// \brief Set the new parameters of the model.</span></div>
<div class="foldopen" id="foldopen00130" data-start="{" data-end="}">
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a91a6fef53ab446352563474ea741e8b2">  130</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a91a6fef53ab446352563474ea741e8b2" title="Set the new parameters of the model.">setParameterVector</a>(<a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a1a6bf16a226df283254eed73c480b280">ParameterVectorType</a> <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(newParameters.size() == <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a581e85d08aae72012f646c39eaf83661" title="Return the number of parameters.">numberOfParameters</a>());</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        noalias(m_filters) = subrange(newParameters,0,m_filters.size());</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        noalias(m_offset) = subrange(newParameters,m_filters.size(),newParameters.size());</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        updateBackpropFilters();</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>    }</div>
</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="comment"></span> </div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment">    /// \brief Return the number of parameters.</span></div>
<div class="foldopen" id="foldopen00138" data-start="{" data-end="}">
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a581e85d08aae72012f646c39eaf83661">  138</a></span><span class="comment"></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a581e85d08aae72012f646c39eaf83661" title="Return the number of parameters.">numberOfParameters</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        <span class="keywordflow">return</span> m_filters.size() + m_offset.size();</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    }</div>
</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="comment"></span> </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span><span class="comment">    ///\brief Sets the structure by setting the shape of image and filters.</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span><span class="comment">    /// \arg imageShape Shape of the image imHeight x imWidth x channel</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment">    /// \arg filterShape Shape of the filter matrix numFilters x fiHeight x fiWidth</span></div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">    /// \arg type Type of convolution padding to perform</span></div>
<div class="foldopen" id="foldopen00147" data-start="{" data-end="}">
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a7e816b41a71a79b5f9da6bf304837b21">  147</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a7e816b41a71a79b5f9da6bf304837b21" title="Sets the structure by setting the shape of image and filters.">setStructure</a>(</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <span class="keyword">const</span>&amp; imageShape, <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <span class="keyword">const</span>&amp; filterShape, <a class="code hl_enumeration" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035">Padding</a> type = <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a5281170e19a4d157693a263c68546214">Padding::ZeroPad</a></div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    ){</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        m_type = type;</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        m_imageHeight = imageShape[0];</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        m_imageWidth = imageShape[1];</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        m_numChannels = imageShape[2];</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        m_numFilters = filterShape[0];</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        m_filterHeight = filterShape[1];</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        m_filterWidth = filterShape[2];</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        m_filters.resize(m_filterHeight * m_filterWidth * m_numFilters * m_numChannels);</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        m_offset.resize(m_numFilters);</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        updateBackpropFilters();</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>    }</div>
</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span> </div>
<div class="foldopen" id="foldopen00162" data-start="{" data-end="}">
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#aa837cb1b2e7f96cee6384c1464ad0bfb">  162</a></span>    boost::shared_ptr&lt;State&gt; <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#aa837cb1b2e7f96cee6384c1464ad0bfb" title="Creates an internal state of the model.">createState</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        <span class="keywordflow">return</span> boost::shared_ptr&lt;State&gt;(<span class="keyword">new</span> <span class="keyword">typename</span> ActivationFunction::State());</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>    }</div>
</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span> </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    <span class="keyword">using </span><a class="code hl_function" href="classshark_1_1_abstract_model.html#ac7edef74da55322b6aef0ba65b08592d" title="Standard interface for evaluating the response of the model to a batch of patterns.">base_type::eval</a>;</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span><span class="comment"></span> </div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span><span class="comment">    /// Evaluate the model</span></div>
<div class="foldopen" id="foldopen00169" data-start="{" data-end="}">
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a8465955f88c4eeb452342583b2dcb3d3">  169</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a8465955f88c4eeb452342583b2dcb3d3" title="Evaluate the model.">eval</a>(<a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#ae73b28ed0cf6fa128617f5ec57890362">BatchInputType</a> <span class="keyword">const</span>&amp; inputs, <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a>&amp; outputs, <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>&amp; state)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(inputs.size2() == <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a9cfa029e47ed8ede811b331830a45061" title="Returns the expected shape of the input.">inputShape</a>().numElements());</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        outputs.resize(inputs.size1(),<a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>().numElements());</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        outputs.clear();</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <span class="comment">//geometry for &quot;zero pad&quot;</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        std::size_t outputsForFilter = <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>().<a class="code hl_function" href="classshark_1_1_shape.html#ad36fc62c674b01150cc5addab9dcc38d">numElements</a>()/m_numFilters;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>        std::size_t paddingHeight = (m_type != <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a3ac705f2acd51a4613f9188c05c91d0d">Padding::Valid</a>) ? m_filterHeight - 1: 0;</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        std::size_t paddingWidth = (m_type != <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a3ac705f2acd51a4613f9188c05c91d0d">Padding::Valid</a>) ? m_filterWidth - 1: 0;</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        </div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        blas::kernels::conv2d(inputs, m_filters, outputs,</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>            m_numChannels, m_numFilters, </div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>            m_imageHeight, m_imageWidth,</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            m_filterHeight, m_filterWidth,</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>            paddingHeight, paddingWidth</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        );</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>        <span class="comment">//reshape matrix for offset</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>        <span class="keyword">auto</span> reshapedOutputs = to_matrix(to_vector(outputs), outputsForFilter * inputs.size1(), m_numFilters);</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        noalias(reshapedOutputs ) += blas::repeat(m_offset,outputsForFilter * inputs.size1());</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        m_activation.evalInPlace(outputs, state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;<span class="keyword">typename</span> ActivationFunction::State&gt;());</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    }</div>
</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span><span class="comment"></span> </div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="comment">    ///\brief Calculates the first derivative w.r.t the parameters and summing them up over all inputs of the last computed batch</span></div>
<div class="foldopen" id="foldopen00191" data-start="{" data-end="}">
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a7e08e22875a1316ee70df6fb857900c6">  191</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a7e08e22875a1316ee70df6fb857900c6" title="Calculates the first derivative w.r.t the parameters and summing them up over all inputs of the last ...">weightedParameterDerivative</a>(</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#ae73b28ed0cf6fa128617f5ec57890362">BatchInputType</a> <span class="keyword">const</span>&amp; inputs,</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> <span class="keyword">const</span>&amp; outputs,</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> <span class="keyword">const</span>&amp; coefficients,</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a1a6bf16a226df283254eed73c480b280">ParameterVectorType</a>&amp; gradient</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(coefficients.size2()==<a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>().numElements());</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(coefficients.size1()==inputs.size1());</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        std::size_t n = inputs.size1();</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        <span class="keyword">auto</span> outputHeight = <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>()[0]; </div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        <span class="keyword">auto</span> outputWidth = <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>()[1]; </div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> delta = coefficients;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>        m_activation.multiplyDerivative(outputs,delta, state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;<span class="keyword">typename</span> ActivationFunction::State&gt;());</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>        </div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>        gradient.resize(<a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a581e85d08aae72012f646c39eaf83661" title="Return the number of parameters.">numberOfParameters</a>());</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        <span class="keyword">auto</span> weightGradient = subrange(gradient,0,m_filters.size());</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        <span class="keyword">auto</span> offsetGradient = subrange(gradient, m_filters.size(),gradient.size());</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        </div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        std::size_t paddingHeight = (m_type != <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a3ac705f2acd51a4613f9188c05c91d0d">Padding::Valid</a>) ? m_filterHeight - 1: 0;</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        std::size_t paddingWidth = (m_type != <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a3ac705f2acd51a4613f9188c05c91d0d">Padding::Valid</a>) ? m_filterWidth - 1: 0;</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        </div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        <span class="comment">//derivatives of offset parameters</span></div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <span class="comment">//reshape coefficient matrix  into a matrix where the rows are the single output pixels</span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <span class="keyword">auto</span> delta_pixels = to_matrix(to_vector(delta), coefficients.size1() * coefficients.size2()/m_numFilters, m_numFilters);</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        noalias(offsetGradient) = sum(as_columns(delta_pixels));</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        </div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        <span class="comment">//derivative of filters:</span></div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        <span class="comment">//the idea is to phrase this derivative in terms of another convolution.</span></div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        <span class="comment">//for this we swap for coefficients and inputs the batch-size with the number of channels</span></div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <span class="comment">// i.e. we transform NHWC to CHWN.</span></div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>        <span class="comment">// afterwards the derivative is just convolving the coefficients with the inputs (padding the inputs as normal).</span></div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>        <span class="comment">// after convolving, the output has the filters as channels, therefore the derivative has to be reordered back</span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>        <span class="comment">// from CHWN to NHWC format.</span></div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> delta_CHWN(m_numFilters, outputHeight * outputWidth * n);</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> inputs_CHWN(m_numChannels, <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a9cfa029e47ed8ede811b331830a45061" title="Returns the expected shape of the input.">inputShape</a>().numElements() / m_numChannels * n);</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        image::reorder&lt;value_type, device_type&gt;(</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>            to_vector(delta), to_vector(delta_CHWN), </div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>            {n, outputHeight, outputWidth, m_numFilters},</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>            <a class="code hl_enumvalue" href="namespaceshark.html#a714312a12ebf808461e1c6ecdf122a18ad066db54b89b0912e7e7c6da51e2da51">ImageFormat::NHWC</a>, <a class="code hl_enumvalue" href="namespaceshark.html#a714312a12ebf808461e1c6ecdf122a18a06de70c18443a30c80a3418890ac1cab">ImageFormat::CHWN</a></div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>        );</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        image::reorder&lt;value_type, device_type&gt;(</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>            to_vector(inputs), to_vector(inputs_CHWN),</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>            {n, m_imageHeight, m_imageWidth, m_numChannels},</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>            <a class="code hl_enumvalue" href="namespaceshark.html#a714312a12ebf808461e1c6ecdf122a18ad066db54b89b0912e7e7c6da51e2da51">ImageFormat::NHWC</a>, <a class="code hl_enumvalue" href="namespaceshark.html#a714312a12ebf808461e1c6ecdf122a18a06de70c18443a30c80a3418890ac1cab">ImageFormat::CHWN</a></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>        );</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#ae73b28ed0cf6fa128617f5ec57890362">BatchInputType</a> responses_CHWN(m_numChannels, m_filters.size() / m_numChannels);</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>        blas::kernels::conv2d(inputs_CHWN, to_vector(delta_CHWN), responses_CHWN,</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>            n, m_numFilters, </div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>            m_imageHeight, m_imageWidth,</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>            outputHeight, outputWidth,</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>            paddingHeight, paddingWidth</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        );</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        image::reorder&lt;value_type, device_type&gt;(</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>            to_vector(responses_CHWN), weightGradient, </div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>            {m_numChannels, m_filterHeight, m_filterWidth, m_numFilters}, </div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>            <a class="code hl_enumvalue" href="namespaceshark.html#a714312a12ebf808461e1c6ecdf122a18a06de70c18443a30c80a3418890ac1cab">ImageFormat::CHWN</a>, <a class="code hl_enumvalue" href="namespaceshark.html#a714312a12ebf808461e1c6ecdf122a18ad066db54b89b0912e7e7c6da51e2da51">ImageFormat::NHWC</a></div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        );</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span><span class="comment">    ///\brief Calculates the first derivative w.r.t the inputs and summs them up over all inputs of the last computed batch</span></div>
<div class="foldopen" id="foldopen00251" data-start="{" data-end="}">
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a58ea7c864d76478c172a7e7799ad816d">  251</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a58ea7c864d76478c172a7e7799ad816d" title="Calculates the first derivative w.r.t the inputs and summs them up over all inputs of the last comput...">weightedInputDerivative</a>(</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#ae73b28ed0cf6fa128617f5ec57890362">BatchInputType</a> <span class="keyword">const</span> &amp; inputs,</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> <span class="keyword">const</span>&amp; outputs,</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> <span class="keyword">const</span> &amp; coefficients,</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#ae73b28ed0cf6fa128617f5ec57890362">BatchInputType</a>&amp; derivatives</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(coefficients.size2() == <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>().numElements());</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(coefficients.size1() == inputs.size1());</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        </div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        <a class="code hl_typedef" href="classshark_1_1_conv2_d_model.html#a3d8e691008283fe9b6a68bd542c86fc0">BatchOutputType</a> delta = coefficients;</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        m_activation.multiplyDerivative(outputs,delta, state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;<span class="keyword">typename</span> ActivationFunction::State&gt;());</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> shape = <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ac9d63e4f9c8de8c29fe5995555581f4f" title="Returns the shape of the output.">outputShape</a>();</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        std::size_t paddingHeight = m_filterHeight - 1;</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        std::size_t paddingWidth = m_filterWidth - 1;</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <span class="keywordflow">if</span>(m_type == <a class="code hl_enumvalue" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035a3ac705f2acd51a4613f9188c05c91d0d">Padding::Valid</a>){</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>            paddingHeight *=2;</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>            paddingWidth *=2;</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        }</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>        derivatives.resize(inputs.size1(),<a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a9cfa029e47ed8ede811b331830a45061" title="Returns the expected shape of the input.">inputShape</a>().numElements());</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>        derivatives.clear();</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        blas::kernels::conv2d(delta, m_backpropFilters, derivatives,</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>            m_numFilters, m_numChannels, </div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>            shape[0], shape[1],</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>            m_filterHeight, m_filterWidth,</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>            paddingHeight, paddingWidth</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        );</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>    }</div>
</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span><span class="comment"></span> </div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span><span class="comment">    /// From ISerializable</span></div>
<div class="foldopen" id="foldopen00281" data-start="{" data-end="}">
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#ae6bb09a47a944952e12a735fc1be7d43">  281</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#ae6bb09a47a944952e12a735fc1be7d43" title="From ISerializable.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a>&amp; archive){</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span>        archive &gt;&gt; m_filters;</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>        archive &gt;&gt; m_offset;</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>        archive &gt;&gt; m_imageHeight;</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>        archive &gt;&gt; m_imageWidth;</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>        archive &gt;&gt; m_filterHeight;</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>        archive &gt;&gt; m_filterWidth;</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>        archive &gt;&gt; m_numChannels;</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>        archive &gt;&gt; m_numFilters;</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>        archive &gt;&gt; (<span class="keywordtype">int</span>&amp;) m_type;</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>        updateBackpropFilters();</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span><span class="comment">    /// From ISerializable</span></div>
<div class="foldopen" id="foldopen00294" data-start="{" data-end="}">
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"><a class="line" href="classshark_1_1_conv2_d_model.html#a8feb74e6ec2f1ef0331b78553f142bcb">  294</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_conv2_d_model.html#a8feb74e6ec2f1ef0331b78553f142bcb" title="From ISerializable.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a>&amp; archive)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>        archive &lt;&lt; m_filters;</div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>        archive &lt;&lt; m_offset;</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>        archive &lt;&lt; m_imageHeight;</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>        archive &lt;&lt; m_imageWidth;</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>        archive &lt;&lt; m_filterHeight;</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>        archive &lt;&lt; m_filterWidth;</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>        archive &lt;&lt; m_numChannels;</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>        archive &lt;&lt; m_numFilters;</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>        archive &lt;&lt; (<span class="keywordtype">int</span>&amp;) m_type;</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>    }</div>
</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>    </div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>    <span class="comment"></span></div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span><span class="comment">    ///\brief Converts the filters into the backprop filters</span></div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span><span class="comment">    /// for computing the derivatie wrt the inputs in the chain rule, we </span></div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span><span class="comment">    /// have to convove the outer derivative with the &quot;transposed&quot; filters</span></div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span><span class="comment">    /// the transposition is done by switching the order of channels and filters in the storage</span></div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span><span class="comment"></span>    <span class="keywordtype">void</span> updateBackpropFilters(){</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>        m_backpropFilters.resize(m_filters.size());</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>        </div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>        std::size_t filterImSize = m_filterWidth * m_filterHeight;</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>        std::size_t filterSize = m_numChannels * m_filterWidth * m_filterHeight;</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>        std::size_t bpFilterSize = m_numFilters * m_filterWidth * m_filterHeight;</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>        </div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>        <span class="comment">//Note: this looks a bit funny, but this way on a gpu only m_numChannel kernels need to be run</span></div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>        <span class="keywordflow">for</span>(std::size_t c = 0; c != m_numChannels; ++c){</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>            <span class="keyword">auto</span> channel_mat = subrange(</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>                to_matrix(m_filters, m_numFilters, filterSize), <span class="comment">//create matrix where each row is one filter</span></div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>                0, m_numFilters, c * filterImSize, (c+1) * filterImSize <span class="comment">//cut out all columns belonging to the current channel</span></div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>            );</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>            <span class="comment">//Todo: commented out, because we also need to flip, which is not implemented in remora</span></div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>            <span class="comment">//~ auto channel_vec = to_vector(flip(channel_mat));//flip and linearize to vector (flip not implemented)</span></div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>            <span class="comment">//~ //cut out target are and set values</span></div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>            <span class="comment">//~ noalias(subrange(m_backpropFilters, c * bpFilterSize, (c+1) * bpFilterSize)) = channel_vec;</span></div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>            <span class="comment">//instead use this cpu-only version</span></div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>            <span class="keyword">auto</span> target_vec = subrange(m_backpropFilters, c * bpFilterSize, (c+1) * bpFilterSize);</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>            <span class="keyword">auto</span> target_mat = to_matrix(target_vec,m_numFilters, m_filterWidth * m_filterHeight);</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>            <span class="keywordflow">for</span>(std::size_t f = 0; f != m_numFilters; ++f){</div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>                <span class="keywordflow">for</span>(std::size_t i = 0; i !=  m_filterWidth * m_filterHeight; ++i){</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>                    target_mat(f,i) = channel_mat(f, m_filterWidth * m_filterHeight-i-1);</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>                }</div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span>            }</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span>        }</div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>    }</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>    <a class="code hl_typedef" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a> m_filters; <span class="comment">///&lt; Filters used for performing the convolution</span></div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>    <a class="code hl_typedef" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a> m_backpropFilters;<span class="comment">///&lt; Same as filter just with the storage order of filters and channels reversed.</span></div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span>    <a class="code hl_typedef" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a> m_offset;<span class="comment">///&lt; offset applied to each filters response</span></div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span>    ActivationFunction m_activation;<span class="comment">///&lt; The activation function to use</span></div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span> </div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>    std::size_t m_imageHeight;</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>    std::size_t m_imageWidth;</div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span>    std::size_t m_filterHeight;</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>    std::size_t m_filterWidth;</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>    std::size_t m_numChannels;</div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>    std::size_t m_numFilters;</div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span>    </div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>    <a class="code hl_enumeration" href="namespaceshark.html#a7d0c8762c37c217fdb30def7e3059035">Padding</a> m_type;</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>};</div>
</div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span> </div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span> </div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>}</div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span><span class="preprocessor">#endif</span></div>
</div><!-- fragment --></div><!-- contents -->
</div>
</body>
</html>
